The need to equalize signals in high speed communication has been described by J. Liu; et al. in an article titled “Equalization in High-Speed Communication Systems”, published in the IEEE Circuits and Systems Magazine, pages 4-17, 2004.
Wireline communication systems consist conceptually of three different building blocks: a transmitter (TX), a channel (e.g. a cable or optical fiber) and a receiver (RX). Due to non-ideal channel characteristics like limited bandwidth and crosstalk noise, the RX input signals are deteriorated such that the data recovery on the receiver side ends up with unreasonable bit error rates (BER). Increasing system bandwidth requirements in combination with longer transmission lines makes the above mentioned problem worse.
Usually, of major concern is the limited channel bandwidth, which causes inter-symbol interference (ISI). This is due to the fact that the binary data pattern (e.g. NRZ or RZ pulses) contains many different frequency components that suffer from dispersion after transmission via the channel. A single “0” or “1” after a long data string of ones or zeros respectively, might not reach/exceed the switching threshold.
This means that the data eye is completely closed. Therefore, reliable data recovery is impossible and accordingly the BER is downgraded. Therefore, channel equalization is mandatory in order to restore timing (and amplitude) information which improve the receive signal quality and therewith the BER. Channel equalization can be done on transmitter side using pre-emphasis or on receiver side employing post-equalization. A combination of both techniques features highest performance.
Of major concern is the adaptation or tuning of the equalizer transfer function according to the used cable length and data rate. For many applications this has to be done without any information about transmission channel (length and performance) or transmitted data. In the past, this has been accomplished in several ways:    1. By analyzing the received signal power and adjusting an analog or digital filter according to the required power level, which is e.g. stored in the memory.    2. Analyze the equalized data on short and long term (e.g. by low pass and high pass filtering and succeeding peak detectors) such that an analog “error” signal is generated to tune the equalizer's transfer function.    3. Calculate the mean square error between the recovered data and a (known) training sequence.    4. Or estimate the channel impulse response (by discrete Fourier transform and its inverse) and adjust the coefficients ( ) of the FIR filter and the coefficients (An) of the IIR filter to counteract the channel losses.
Some of these methods are not practicable in some applications e.g. when no equalizer training sequence is intended in the specification. Another technique mentioned above compares the signal power of the incoming data with values stored in a memory. Here, data dependent errors cause equalizer tuning misalignment, since the received power depends also on the transmitted sequence/order of bits. Secondly a ROM/RAM memory is required.
Furthermore, most of the prior art methods require a lot of analog hardware in terms of filters, peak detectors, amplifiers etc. such that these techniques consume a lot of power and silicon area.
Therefore, the present state-of-the-art equalizer tuning algorithms are either restricted to certain applications or they require a significant amount of power and area consuming analog circuitry. The latter is usually also sensitive to PVT (process, supply voltage and temperature). The new technique proposed here, employs merely digital circuitry, requires only small silicon area and is power efficient, compared to competing solutions. Therefore this invention provides a simple and robust solution to adjust autonomous the cable equalizer and if wanted to select simultaneously the best sampling phase.
The proposed blind equalizer tuning algorithm can be applied to oversampled receiver front-ends.